The Difference the Body Makes: The Teacher’s Presence Online and Offline
Bibliographic record
Abstract
This paper examines the question: what is the experience of meeting online and how does it differ from ordinary classroom situations? Drawing from personal experience, the author explores possible experiences of existing in virtual space and time. How do people meet, get to know each other and, interact in a pedagogical situation? Her experience as an online student made her to seriously reflect on the experiential nature of the computer-mediated encounter. But, it was not until she happened to participate in a workshop offered by the same teacher that the contrasts began to take shape for her. If there is a difference between online and offline meetings, what is it that makes the difference? Online communication could, just as face-to-face meetings, create feelings of closeness, and friendship; from the other-as-a-text on the screen, we subjectively create the other-as-an-idea, an idea that might be perceived as the real other. But is it? What reality is for real? What is the nature of the relationship established between body-less persons on line, and what difference does the body make in a face-to-face meeting?
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".